September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
Content preview from 基于AWS的数据科学实践
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数据科学的应用场景
｜
69
2.15
认知式的预测性商业智能
许多机器学习应用和模型都假设数据已经在数据湖中且易于获取（如第
4
章所述）。
然而现实中，世界上许多数据都在结构化的关系型数据库中保存并处理。为了能够
在这些结构化数据上应用机器学习，我们需要将数据导出，或开发一个自定义的应
用在应用任何机器学习之前读取数据。如果能够直接在商业智能服务、数据仓库或
数据库上使用机器学习，那会不会很棒？让我们看看如何通过
AWS
实现这点。
2.15.1
用
Amazon QuickSight
询问自然语言问题
Amazon QuickSight
是一个商业智能服务，其运行交互式查询，并为像
Amazon
Redshift
、
Amazon RDS
、
Amazon Athena
和
Amazon S3
这样的数据源构建可视化。
QuickSight
也可以通过
QuickSight ML Insights
和
QuickSight Q
来从我们的数据中
检测异常、创建预报和回答自然语言问题。
QuickSight ML Insights
运行
RCF
算法从数十亿数据点和数以百万的指标当中识别
变化。
ML Insights
也支持基于观测到的指标进行预报。
RCF
算法从数据中自动检测
季节性模式，排除离群值并推算缺失值。
通过
QuickSight Q
，我们可以询问自然语言问题，比如“美国加利福尼亚州最畅
销的产品类别是什么？”
QuickSight
使用机器学习来理解问题、将问题应用到数
据并创建一个图表来回答我们的问题，如图
2-6
所示。我们将会在第
5
章深入探讨 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.